prototypes/dynamic_dml/dynamic_panel_dgp.py [178:206]:
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        self.Beta = autoreg * np.eye(self.n_x)

        self.epsilon = np.random.uniform(-1, 1, size=self.n_treatments)
        self.zeta = np.zeros(self.n_x)
        self.zeta[:self.s_x] = self.conf_str / self.s_x

        self.y_hetero_effect = np.zeros(self.n_x)
        self.x_hetero_effect = np.zeros(self.n_x)
        if self.hetero_inds is not None:
            self.y_hetero_effect[self.hetero_inds] = np.random.uniform(.5 * hetero_strength, 1.5 * hetero_strength) / \
                len(self.hetero_inds)
            self.x_hetero_effect[self.hetero_inds] = np.random.uniform(.5 * hetero_strength, 1.5 * hetero_strength) / \
                len(self.hetero_inds)

        self.true_effect = np.zeros((self.n_periods, self.n_treatments))
        self.true_effect[0] = self.epsilon
        for t in np.arange(1, self.n_periods):
            self.true_effect[t, :] = (self.zeta.reshape(
                1, -1) @ np.linalg.matrix_power(self.Beta, t - 1) @ self.Alpha)

        self.true_hetero_effect = np.zeros(
            (self.n_periods, (self.n_x + 1) * self.n_treatments))
        self.true_hetero_effect[0, :] = cross_product(add_constant(self.y_hetero_effect.reshape(1, -1), has_constant='add'),
                                                      self.epsilon.reshape(1, -1))
        for t in np.arange(1, self.n_periods):
            self.true_hetero_effect[t, :] = cross_product(add_constant(self.x_hetero_effect.reshape(1, -1), has_constant='add'),
                                                          self.zeta.reshape(1, -1) @ np.linalg.matrix_power(self.Beta, t - 1) @ self.Alpha)

        return self
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prototypes/dynamic_dml/dynamic_panel_dgp.py [229:257]:
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        self.Beta = autoreg * np.eye(self.n_x)

        self.epsilon = np.random.uniform(-1, 1, size=self.n_treatments)
        self.zeta = np.zeros(self.n_x)
        self.zeta[:self.s_x] = self.conf_str / self.s_x

        self.y_hetero_effect = np.zeros(self.n_x)
        self.x_hetero_effect = np.zeros(self.n_x)
        if self.hetero_inds is not None:
            self.y_hetero_effect[self.hetero_inds] = np.random.uniform(.5 * hetero_strength, 1.5 * hetero_strength) / \
                len(self.hetero_inds)
            self.x_hetero_effect[self.hetero_inds] = np.random.uniform(.5 * hetero_strength, 1.5 * hetero_strength) / \
                len(self.hetero_inds)

        self.true_effect = np.zeros((self.n_periods, self.n_treatments))
        self.true_effect[0] = self.epsilon
        for t in np.arange(1, self.n_periods):
            self.true_effect[t, :] = (self.zeta.reshape(
                1, -1) @ np.linalg.matrix_power(self.Beta, t - 1) @ self.Alpha)

        self.true_hetero_effect = np.zeros(
            (self.n_periods, (self.n_x + 1) * self.n_treatments))
        self.true_hetero_effect[0, :] = cross_product(add_constant(self.y_hetero_effect.reshape(1, -1), has_constant='add'),
                                                      self.epsilon.reshape(1, -1))
        for t in np.arange(1, self.n_periods):
            self.true_hetero_effect[t, :] = cross_product(add_constant(self.x_hetero_effect.reshape(1, -1), has_constant='add'),
                                                          self.zeta.reshape(1, -1) @ np.linalg.matrix_power(self.Beta, t - 1) @ self.Alpha)

        return self
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